The subject matter disclosed herein generally relates to determination of vital parameters from patient data. More specifically, the subject matter relates to a method and system for determination of respiration rate from a plurality of signals obtained from a patient.
Respiratory rate or the breathing rate is a potent indicator of patient health with relevance to respiratory and cardiovascular functions. In fact, respiratory rate exceeding 27 breaths per minute has been found to be a predictor of cardiac arrests in hospitals. Respiration rate measurement is useful in designing early warning scoring systems (EWS) for critical illness. In spite of its importance, respiration rate is often a neglected vital sign and is not routinely measured in clinical practice. One reason for this is that the manual measurement of respiration rate (e.g., counting breaths at the patient bedside) is a cumbersome process and also does not yield continuous estimates. That is, there are often periods of time where the patient is left unmonitored. As such, the manual measurement is associated with a high likelihood of missing important respiration events during the unmonitored period. Conventional methods used for unobtrusive continuous estimation of respiration rate suffer from poor measurement accuracy owing largely to algorithmic insufficiency. Presence of artifact signals such as cardiac artifacts and motion artifacts require computationally intensive and sophisticated signal processing techniques for deriving an estimate of respiration rate. Measurement inaccuracy can trigger false alarms often perpetrating the problem of “alarm fatigue”, wherein caregivers have the tendency to ignore critical events with the belief that the measurement estimate is in all likelihood inaccurate.
There is a need for an enhanced system and method for estimation of respiration rate.